Fast Fourier Transform or FFT is at the heart of any DSP system. Converting to frequency domain and then sampling was a challenge overcome using DFT. But in today's world of real time processing DFT is too slow. If we want to, say, analyse the vibrations of a railway track caused by a train, then, if the DFT algorithm is started when the train approaches the track, two other trains would pass by before analysis is complete.
This definitely won't do. So FFT, which as the name suggests is a fast algorithm, is used. We studied implementing Cooley and Tukey's Radix-2 DITFFT algorithm. DIT stands for Decimation In Time. The signal is decimated in time domain which helps to reduce the number of calculations. We observed that the number of complex multiplications reduced greatly. This contributes to the speed. And the input and output sequence orders are in a bit reversed manner. So this model is easy to expand to higher values of N and improve the computational efficiency of DSP systems.
Decomposition reduces calculation
ReplyDeleteYes as we split the signal into parts
DeleteFFT is computationally faster than DFT
ReplyDeleteHelps to reduce a lot of processing time
DeleteThanks
ReplyDeleteIn real time systems FFT is calculated for every signal then processed further.
ReplyDeleteFast Fourier Transform is more effective in a practical approach when code is executed and implemented
ReplyDeleteYeah
DeleteParallel processing helps to improve speed also no of computations required are less than DFT.
DeleteRight
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